Pinching-antenna systems (PASs) can overcome signal blockage by repositioning dielectric radiating elements, called pinching antennas (PAs), along meter-scale waveguides to create line-of-sight links. Since each waveguide is driven by a single radio-frequency (RF) chain, non-orthogonal multiple access (NOMA) is well suited for PAS-based multi-user communications. This paper studies a PAS-enabled multi-user downlink NOMA system with multiple waveguides, each equipped with multiple PAs. The PA positions and base-station transmit precoding are jointly optimized to maximize the minimum user rate. The resulting problem is highly non-smooth and non-convex because of the rapidly oscillating coherent sums caused by inter-PA interference. To tackle this challenge, we propose a two-stage structured optimization framework. In the first stage, coarse PA-position and power-allocation optimization is performed using an interior-point algorithm while neglecting the PA channel phases, which gives solutions near the true optima. In the second stage, PA positions and transmit precoding are fine-tuned while accounting for the PA channel phase shifts. This stage first applies phase zeroing, where each PA is locally repositioned to align the corresponding channel phase toward zero and promote constructive coherent combining. It then uses an alternating procedure that iteratively performs forward-backward PA position refinement and successive-convex-approximation-based complex transmit precoding optimization until convergence, thereby reducing residual phase mismatch. Simulation results show that the proposed framework significantly outperforms heuristic optimization benchmarks with much lower computational time. They also demonstrate large gains over a comparable multiple-input multiple-output downlink NOMA system and reveal the impact of the number of PAs, users, and transmit power on system performance.

This paper studies a free-space optical (FSO) link assisted by an optical reconfigurable intelligent surface (ORIS) and enhanced by a hybrid automatic repeat request (HARQ) scheme. The ORIS creates a virtual line-of-sight path around obstacles, while HARQ recovers frames corrupted by turbulence, pointing jitter, and geometric loss through retransmission and combining. We first derive a tractable statistical model for the end-to-end transmitter-ORIS-receiver (Tx-ORIS-Rx) reflected channel by jointly accounting for atmospheric turbulence, ORIS-induced pointing errors, and geometric attenuation. Building on these results, we obtain closed-form outage probability (OP) expressions for HARQ with Chase combining (HARQ-CC) and analytical outage upper bounds for HARQ with incremental redundancy (HARQ-IR), valid for an arbitrary maximum number of transmission rounds. We further conduct a high signal-to-noise ratio (SNR) analysis that provides a thorough characterization of the outage behavior and reveals the diversity order of both schemes. In addition, we characterize the delay behavior of the truncated HARQ process through the mean number of transmission rounds and the conditional mean number of rounds given successful decoding. Finally, numerical and Monte Carlo results validate the proposed analysis and show that HARQ substantially improves ORIS-assisted FSO reliability, with HARQ-IR achieving lower outage and delay than HARQ-CC, even for a small number of retransmission rounds.

Orthogonal Frequency Division Multiplexing (OFDM)-based integrated sensing and communication systems demand a unified waveform that simultaneously supports reliable data transmission, low peak-to-average power ratio (PAPR), and accurate channel sensing. Existing approaches multiplex communication and sensing across separate time or frequency resources, or rely on dedicated pilots for channel estimation, limiting system flexibility and increasing overhead. This paper proposes an amplitude-phase-frequency block modulation (APFBM) scheme for OFDM that achieves waveform-level integration of communication and sensing without resource partitioning. Information symbols are represented on the Stokes sphere and mapped to energy-normalized Jones vectors through an unambiguous rule that establishes a deterministic phase reference per block. This mapping exposes a commonphase degree of freedom inherent in the signal structure. At the transmitter, a grouped phase optimization algorithm exploits this structural freedom to reduce the PAPR without side information (SI). At the receiver, the same deterministic phase structure enables a Viterbi-based maximum-likelihood (ML) sequence detection algorithm that jointly recovers the optimization phases and estimates the block-wise channel amplitude and phase. No dedicated sensing pilots are required, as the sensing observables are extracted directly from the communication waveform. Closed-form error-rate and sensing-accuracy expressions are derived. Numerical simulations and over-the-air measurements on a software-defined radio link confirm effective PAPR reduction, accurate channel sensing, reliable phase recovery, and stable channel state information reconstruction. The proposed scheme trades a moderate reduction in spectral efficiency for a unified waveform design that simultaneously delivers SI-free PAPR reduction and pilotless sensing.

Wireless foundation models (WFMs) have recently emerged as a promising paradigm for AI-native 6G networks, enabling universal channel representations adaptable to diverse communication and sensing tasks. Existing WFMs are predominantly built upon the Transformer architecture, which delivers superior performance but incurs computational complexity proportional to the square of the input sequence length, posing a significant barrier to their deployment under stringent inference latency constraints. To address this issue, in this paper, we propose ConsisFormer, a compute-efficient Transformer design based on short-term consistency of wireless channels, as a WFM backbone. By utilizing the observation that adjacent time or frequency instances share similar clusters of scatterers and thus exhibit similar channel characteristics, we develop an adaptive token aggregation (ATA) module to dynamically merge neighboring channel state information (CSI) tokens, thereby reducing the length of the token sequence involved in self-attention calculations to lower the computational cost. Furthermore, we propose a feature sequence interpolation (FSI) method to recover the full CSI representation based on the sparse feature sequence outputted from the Transformer blocks, thus keeping the performance unaffected while ensuring low complexity. Moreover, we propose an aggregated auto-encoder (AAE) pre-training paradigm for WFMs, enabling robust channel representation learning from sparsified CSI tokens via compression and recovery. Simulation results show that the proposed design reduces the computational complexity of WFM by over $83\%$ with negligible performance loss on various tasks including channel prediction, LoS/NLOS classification, beam prediction, and localization.

Matched-filter-based pulse-compression distributed acoustic sensing (DAS) suffers from nonzero compression sidelobes that cause deterministic inter-range-bin leakage, i.e., spatial inter-symbol interference (ISI), and false responses in reconstructed Rayleigh-backscatter traces. We propose a cyclic-prefix orthogonal frequency-division multiplexing (CP-OFDM) DAS system for $ϕ$-OTDR, using a data-bearing CP-OFDM waveform as the sensing probe. It also recovers forward communication data, providing an initial demonstration of shared-waveform integrated sensing and communication (ISAC). To our knowledge, this is the first formulation of distributed Rayleigh backscattering as a finite-memory sensing multipath channel. Based on this formulation, we prove that, if the useful OFDM and CP lengths cover the sensing multipath memory, CP removal, one-tap frequency-domain equalization, and inverse discrete Fourier transform reconstruct each range-bin coefficient without deterministic waveform-induced spatial ISI, enabling spatial-ISI-free phase demodulation. For a simulated 5.2-km link with ten simultaneous strong and weak events spaced by 5.31--5.83 m within groups, the proposed receiver suppresses off-event leakage and improves phase-trace mean-square error by up to 29.55 dB over matched-filter pulse compression. In a heterodyne coherent experiment over a 5.2-km fiber link with 111.984-MHz occupied bandwidth, 500-Hz PZT vibrations are blindly localized at 5.071 and 5.066 km under 5- and 1-V drives, respectively, and their waveforms are recovered with correlation coefficients of 0.990 and 0.962. The same data-bearing probe also recovers an image with zero measured bit-error rate and a median error vector magnitude of -23.14 dB. These results validate CP-OFDM-aided frequency-domain channel reconstruction for spatial-ISI-free DAS and demonstrate its potential for shared-waveform optical-fiber ISAC.

This paper studies a class of parametric constrained optimization problems that are motivated by applications in real time applications. Under a parameter-separable problem structure that naturally arises in these applications, the paper proposes a two phase strategy, based on offline learning and online processing, to address these optimization problems on resource limited devices. Specifically, by exploiting the separable structure, an iterative Alternating Direction Method of Multipliers (ADMM) based solution procedure is developed that enables the use of certain learning based function representations (learned offline but readily computable online) to reduce the overall online on-device implementation complexity. By carefully crafting the ADMM procedure, it is shown that even as the parameters vary, the corresponding instances of the parametric optimization problem may be solved by lightweight online computations in the device with the assistance of a neural network co-processor.

Spatial degrees of freedom (DoF), sampling, and beamforming are fundamental to multi-user large intelligent surfaces (LISs), where electromagnetic fields must be shaped, resolved, and focused at multiple near-field locations. This work estimates the number of DoF using closed-form expressions derived from the mutual shadow area for representative LIS configurations. The resulting DoF predictions are validated through numerical singular-value spectra, whose spectral knee points closely match the theoretical estimates. For line-source configurations, an analytic sampling scheme is developed by partitioning the source or observation line into unit-DoF intervals, enabling the selection of spatial samples. Beamforming results using maximum-ratio transmission and zero-forcing demonstrate that approximately the number of DoF independent beams can be formed. Attempting to exceed this limit results in increased interference and degraded performance. For surface-based LIS configurations, sampling points are instead determined numerically using the discrete empirical interpolation method. The corresponding beamforming results further confirm that the target region can support approximately as many independent beams as predicted by the DoF analysis. Finally, a polarization-aware study reveals that the electric-field components contribute unequally to the DoF and that the total-field DoF is twice that of a single polarization component.

This work presents the design and measurement of a multistatic J-band radar chipset comprising a transmitter and a receiver MMIC both featuring an integrated $times$16 frequency multiplier chain for low-frequency local-oscillator distribution and scalable radar configurations. Multistatic radar architectures can sustain high transmission power and high receiver sensitivity simultaneously an advantage that is fully leveraged in the present chipset. To this end a four-way power-combining amplifier chain integrated on the transmitter MMIC delivers an output power of 11.2 dBm. The resulting measured EIRP is 41 dBm at 292 GHz with a collimating PTFE lens and 8.8 dBm without a lens. Despite the high frequency-multiplication factor an on-chip harmonic rejection better than 24 dBc was measured while a radiated in-band harmonic rejection of approximately 50 dBc was achieved through multiple filter stages. The receiver MMIC incorporates a three-stage low-noise amplifier and exhibits an overall conversion gain of 43.3 dB at 292 GHz. Integrated on-chip patch antennas facilitate system integration and the use of highly directive dielectric lenses making the chipset suitable for long-range radar measurements which are demonstrated up to 150 m. The MMICs are realized in a 130 nm SiGe BiCMOS technology with an f_T and f_max of 500 GHz and 610 GHz respectively.

Burst suppression (BS) is a clinically relevant electroencephalographic (EEG) pattern used to monitor sedation depth and brain activity in critically ill patients, particularly during induced coma in Intensive Care Units (ICUs). Automatic burst detection remains challenging because BS patterns vary substantially between patients and annotated datasets are scarce. Recently, EEG Foundation Models (FMs) have shown promise across several downstream EEG applications, but their usefulness for BS detection remains unexplored. We present the first study to evaluate EEG FMs for burst detection in reduced-montage ICU EEG without patient-specific calibration. We compare REVE-base, LUNA-large and LuMamba-Tiny with an adaptive thresholding baseline and a task-specific EEGNet baseline. Additionally, we complement conventional EEG window-based classification with event-based burst detection evaluation. This helps assessing clinically whether burst episodes are correctly detected, reducing the impact of expected annotation variability. The best model, REVE-base, achieved the highest event-based F1-score ($0.868 \pm 0.167$) and reduced burst-per-minute error by 52.1% and 36.2% compared to EEGNet and adaptive thresholding respectively, supporting FMs for scalable EEG monitoring in ICU. Ablation experiments showed that full fine-tuning was the most effective adaptation strategy with respect to frozen-backbone training, two-step fine-tuning, and LoRA-based adaptation, improving event-based F1-score over frozen-backbone training by up to $+0.102$ for LUNA-large. With reduced labeled datasets, pretrained REVE-base outperformed random initialization by $+0.723$ event-based F1 points at 25% of the cohort, demonstrating the benefit of pretraining FM representations when adapted to burst detection with limited labeled data.

Integrated Sensing and Communication (ISAC) refers to the capability for the network to provide communications services whilst also being able to sense the environment in a scalable manner. One of the key functions of ISAC is the accurate localization of passive and mobile sensing targets. This paper introduces a novel hybrid TRP-UE sensing mechanism that improves network-based sensing performance. Evaluation results are provided using 3GPP-compliant ISAC channel models. The results demonstrate the significant benefit in complimenting TRP-based sensing with UE-assisted sensing in challenging propagation environments such as indoor factory.

This paper investigates radio stripes (RSs) as a practical realization of generalized pinching antennas and proposes an RS-based generalized pinching-antenna (RS-GPA) framework. Unlike dielectric-waveguide-based passive pinching antennas that rely on passive coupling from a guided wave into free space, RSs employ active antenna processing units (APUs) deployed along a shared cable for local transmission, reception, and signal processing. This cable-like active architecture offers flexible installation and broad frequency applicability, while allowing selected APUs to act as discrete and controllable radiation or reception points for location-flexible wireless access. Based on the proposed RS-GPA framework, we establish the system and channel models by accounting for the distance-dependent APU-user channels. For downlink transmission, we formulate a circuit-power-aware sparse APU activation and beamforming problem and develop a reweighted group-sparse beamforming algorithm. To reveal the activation principle, we analyze the single-user downlink case and characterize when an additional APU should be activated by balancing transmit-power saving and circuit-power cost. Inspired by this insight, a geometry-guided low-complexity multiuser algorithm is proposed. For uplink transmission, we formulate a joint APU activation and user power control problem and develop a geometry-guided sparse activation design. Numerical results show that the proposed RS-GPA framework substantially reduces the total consumed power compared with benchmark schemes, while the geometry-guided algorithm achieves near-identical consumed-power performance to the group-sparse design with significantly lower runtime.

Interference poses a significant challenge to satellite-based positioning systems, making it essential to accurately quantify the effects of specific interference types on receiver performance and the resulting reliability of position computation. In current practice, interference effects are often quantified using receiver-independent metrics, with receiver-specific front-end characteristics either idealized or only implicitly considered. In this paper, we address this limitation by explicitly incorporating receiver-specific front-end characteristics into the computation of interference effects and validating the resulting receiver-dependent analysis experimentally. Therefore, we record a real-world open-field dataset comprising 210 distinct interference scenarios and compute the receiver-dependent spectral separation coefficient (SSC) and interference impact for a specific receiver module. Furthermore, we verify the computation using a controlled dataset generated with a radio frequency constellation simulator (RFCS), employing the same receiver module and replaying similar interferences classes. The comparison of results obtained in both environments demonstrates the robustness of the interference impact computation.

Dysarthric speech recognition is crucial for facilitating effective communication among individuals with dysarthria. However, accurately recognizing dysarthric speech poses significant challenges due to varying severity levels and limited data availability. In this paper, we explore data augmentation techniques for dysarthric automatic speech recognition (ASR) systems by fine-tuning the End-to-End pre-trained Wav2Vec2 model, with a specific focus on severity levels. To address the challenges of data scarcity and the need for extensive data in fine-tuning pre-trained ASR systems for dysarthric speech, we investigate four prominent data augmentation methods: Speaking-Rate Modification (SRM), Pitch Modification (PM), Formant Modification (FM), and vocal tract Length Perturbation (VTLP), tailored to different aspects of dysarthria. The study uses individually fine-tuned Wav2Vec2 models for each severity class as baseline systems. Additionally, we conducted severity-specific fine-tuning of the ASR model using augmented data. Results demonstrate distinct efficacy patterns for each augmentation technique across severity levels. The best WERs were achieved with SRM ($s$=0.8) for \textit{low} (9.02\%) and \textit{medium} (38.11\%) severities, and with PM ($τ$=0.8) for \textit{high} severity (55.15\%), reflecting relative improvements of 30.02\%, 16.64\%, and 15.47\%, respectively. These results confirm the effectiveness of the augmentation methods in improving dysarthric ASR performance.

The challenge associated with recognizing dysarthric speech primarily arises from pronounced acoustic variability attributed to impaired articulatory precision. Past research has demonstrated improved recognition through the use of hybrid DNN/HMM sequence discriminative training. This paper presents a comprehensive investigation of various combinations of acoustic features tailored to different Acoustic Models, offering suitable feature selections for each. The incorporation of Pitch features notably improved recognition performance, especially for sentence recognition tasks involving dysarthric speech. Through a systematic examination of the TORGO database, we have demonstrated the potential to enhance the performance of the state-of-the-art Factorized Time Delay Neural Network (F-TDNN) model for recognizing dysarthric speech. Our methods, implemented with the F-TDNN model, resulted in a 4.65\% relative improvement in isolated word recognition and a 4.63\% relative improvement in sentence recognition for dysarthric speech, compared to previous research. This improvement effectively compensates for speech variability, attributable to our deliberate selection of the number of overlapping frames between consecutive training example chunks.

Self-supervised speech encoders are predominantly trained by predicting discrete hard cluster IDs at masked positions, a recipe that collapses acoustic ambiguity at category boundaries and requires interrupting training to re-cluster the entire corpus between iterations. We introduce S-JEPA, a JEPA-style encoder-predictor pair trained to match the soft posteriors of a Gaussian Mixture Model at masked positions via KL divergence. Training runs as one continuous optimization trajectory in two phases: a fixed GMM over MFCC features, then an online GMM over encoder features, with the input layer selected adaptively from a label-free signal, removing both the offline re-cluster step and the hand-tuned choice of which transformer layer to cluster on. Under the SUPERB protocol, S-JEPA achieves the lowest WER among evaluated SSL methods below 90M parameters and matches HuBERT-Base on emotion recognition at roughly half its parameter count, establishing a new Pareto frontier without offline re-clustering or teacher distillation. An analysis of the predictor's per-frame entropy on held-out speech reveals a bimodal distribution with a substantial minority of frames near the entropy of a perfect two-cluster tie, providing direct empirical evidence that the soft-target objective preserves the acoustic ambiguity that hard targets would collapse. Code is available at https://github.com/gioannides/s-jepa.

Information lattice learning (ILL) learns interpretable rules of a signal by alternately projecting the signal onto a partition lattice that encodes a hierarchy of abstractions and lifting selected rules back to the signal domain. When the signal is a probability mass function, we show the probabilistic rules learned by ILL admit a natural probabilistic graphical model (PGM) interpretation and develop this interpretation in detail. A partition in ILL induces a deterministic quotient variable, and a rule is the marginal law of that quotient variable. A rule set is therefore a collection of marginal constraints over interpretable abstractions. General lifting is the feasible family of all joint distributions satisfying those constraints, while special lifting chooses a maximum-ignorance reconstruction, implemented in ILL by an L2 uniformity principle closely related to maximum entropy. Under a Shannon-entropy lifting, the same constraints yield a log-linear factor graph whose factors are indexed by learned abstractions. The information lattice itself, however, is not a Bayesian network: its edges encode refinement and coarsening of abstractions, not conditional dependence. Thus ILL is best viewed as structure learning for interpretable constraint-based factor graphs over quotient variables. This view clarifies how ILL relates to graphical models and maximum entropy models, while suggesting new directions for inference, identifiability, and hybrid symbolic-probabilistic learning.

This paper explores the use of generative models to synthesize high-quality, site-specific multiple-input multiple-output (MIMO) channel data, addressing the high cost of the extensive measurement campaigns required to acquire real-world data for AI-native wireless networks. Two location-conditioned generative paradigms are compared: a conditional denoising diffusion implicit model (cDDIM), and a conditional flow matching model (cFMM). Both these models generate MIMO channel matrices conditioned on user coordinates, to preserve the spatial structure of the deployment site. The approaches are evaluated across three dimensions: statistical fidelity (including beam consistency and effective rank), generation efficiency, and utility in downstream tasks such as channel-state information compression and beam alignment. Results across diverse propagation scenarios (28 GHz and 3.5 GHz, both line-of-sight and non-line-of-sight) demonstrate that both models accurately capture site-specific characteristics, even when trained on scarce ground-truth data. Notably, cFMM achieves a quality comparable to cDDIM with roughly an order of magnitude less inference time. Augmenting scarce site-specific datasets with these synthetic channels yields hefty performance gains in downstream physical layer tasks compared to using scarce data alone or stochastic channels.

Reliable state estimation in robotics and control re quires balancing estimation accuracy against computational cost. While filtering-based methods such as the Extended Kalman Filter (EKF) provide efficient real-time updates, and optimisation based formulations using factor graphs improve global consistency, the role of optimisation scheduling is often treated implicitly rather than examined as an explicit design variable. This paper presents an experimental study that explicitly isolates optimisation scheduling using a Smart Scheduled Hybrid (SSH) EKF-FGO framework as a controlled testbed. By combining EKF-based state propagation with periodically invoked batch optimisation and holding solver structure and effort fixed, the main contribution of this work is the experimental characterisation of optimisation scheduling as an independent design variable governing the trade-off between intermediate estimation accuracy and computational cost. Simulation results in a planar SLAM environment show that scheduling strongly influences pre optimisation drift, transient error behaviour, and runtime. In particular, the results identify operating regimes in which most of the benefit of global optimisation can be retained at a fraction of the computational cost, highlighting optimisation scheduling as an under-explored yet critical consideration in hybrid state estimation systems.

This manuscript contains preprint of a chapter under consideration for inclusion in the forthcoming third edition of {\em Cover and Thomas's Elements of Information Theory}, posted with permission from Wiley. The table of contents EIT-3 ToC of the new edition can be found at: https://docs.google.com/document/d/1L-m4oQEJw1PJhoxBeMwrrBD8S_HmvzMEkPbYvS24980/edit?usp=sharing . For feedback, please contact abbas@ee.stanford.edu Learning and information theory intersect in both model training and the characterization of fundamental performance limits. This manuscript provides a concise and accessible treatment of the first intersection, requiring only basic background in information theory and statistics at the senior undergraduate or first-year graduate level. End-of-chapter exercises make the material well suited for classroom use as well as self-study. The chapter focuses on the role of divergence measures in model training, with examples ranging from linear and logistic regression to autoregressive models, variational autoencoders, diffusion models, generative adversarial networks, and score-based models. It introduces the evidence lower bound (ELBO), f-divergences, and the Fisher divergence. In particular, the treatment of the generative diffusion model provides a more systematic and explicit derivation than is typical in the literature.

In this paper, we derive the distribution of a two-dimensional (complex) random walk in which the angle of each step is restricted to a subset of the circle. This setting appears in various domains, such as in over-the-air computation in signal processing. In particular, we derive the exact joint and marginal distributions for two steps, numerical solutions for a general number of steps, and approximations for a large number of steps. Furthermore, we provide an exact characterization of the support for an arbitrary number of steps. The results in this work provide a reference for future work involving such problems.

Reliable flow measurements are essential in many industries, but current instruments often fail to accurately estimate multiphase flows, which are frequently encountered in real-world operations. Combining machine learning (ML) algorithms with accurate single-phase flowmeters has therefore received extensive research attention in recent years. The Coriolis mass flowmeter is a widely used single-phase meter that provides direct mass flow measurements, which ML models can be trained to correct, thereby reducing measurement errors in multiphase conditions. This paper demonstrates that preserving temporal information significantly improves model performance in such scenarios. We compare a multilayer perceptron, a windowed multilayer perceptron, and a convolutional neural network (CNN) on three-phase air-water-oil flow data from 342 experiments. Whereas prior work typically compresses each experiment into a single averaged sample, we instead compute short-time averages from within each experiment and train models that preserve temporal information at several downsampling intervals. The CNN performed best at 0.25 Hz with approximately 95 % of relative errors below 13 %, a normalized root mean squared error of 0.03, and a mean absolute percentage error of approximately 4.3 %, clearly outperforming the best single-averaged model and demonstrating that short-time averaging within individual experiments is preferable. Results are consistent across multiple data splits and random seeds, demonstrating robustness.

We extend the algebraic diversity (AD) framework from classical signal processing to quantum measurement theory. The Quantum Algebraic Diversity (QAD) Theorem establishes that a group-structured positive operator-valued measure (POVM) applied to a single copy of a quantum state produces a full-rank, group-averaged density matrix estimator whose eigenbasis and eigenvalue ordering track those of the true density matrix, with a bias toward the symmetrized state, analogous to the classical recovery of covariance eigenstructure from a single observation. We establish a Classical-Quantum Duality Map connecting classical covariance estimation to quantum state tomography, and an Optimality Inheritance Theorem showing that classical group optimality transfers to quantum settings via the Born map within the group-averaged family. SIC-POVMs are identified as AD with the Heisenberg-Weyl group and mutually unbiased bases as AD with the Clifford group, revealing the hierarchy $\mathrm{HW}(d) \subseteq \mathcal{C}(d) \subseteq S_d$ that mirrors the classical $\mathbb{Z}_M \subseteq G_{\min} \subseteq S_M$. The double-commutator eigenvalue theorem gives polynomial-time adaptive POVM selection. A worked qubit example shows the group-averaged estimator from a single computational-basis measurement, averaged over a matched $\mathbb{Z}_2$ group, reaching fidelity 0.99 where standard single-basis tomography gives a rank-1 estimate of fidelity 0.80. Monte Carlo simulations for $d = 2$ to $13$ confirm fidelity above 0.90 from a single outcome while standard fidelity degrades as $\sim 1/d$. The growing ratio reflects collapse of the rank-1 standard estimator, not fewer copies per parameter: the biased single-copy estimator reduces the number of distinct measurement settings, not the per-parameter sampling cost, and a genuine copy reduction holds only under exact symmetry.

This letter presents a novel approach for \mbox{efficiently} computing time-index powered weighted sums of the form $\sum_{n=0}^{N-1} n^{K} v[n]$ using cascaded accumulators. Traditional direct computation requires $K{\times}N$ general multiplications, which become prohibitive for large $N$, while alternative strategies based on lookup tables or signal reversal require storing entire data blocks. By exploiting accumulator properties, the proposed method eliminates the need for such storage and reduces the multiplicative cost to only $K{+}1$ constant multiplications, enabling efficient real-time implementation. The approach is particularly useful when such sums need to be efficiently computed in sample-by-sample processing systems.

Recent advances in neuromorphic signal processing have introduced time encoding machines as a promising alternative to conventional uniform sampling for low-power communication receivers. In this paradigm, analog signals are converted into event timings by an integrate-and-fire circuit, allowing information to be represented through spike times rather than amplitude samples. While event-driven sampling eliminates the need for a fixed-rate clock, receivers equipped with integrate-and-fire time encoding machines, called time encoding receivers, often assume perfect symbol synchronization, leaving the problem of symbol timing recovery unresolved. This paper presents a joint timing recovery and data detection framework for integrate-and-fire time encoding receivers. The log-likelihood function is derived to capture the dependence between firing times, symbol timing offset, and transmitted sequence, leading to a maximum likelihood formulation for joint timing estimation and sequence detection. A practical two-stage receiver is developed, consisting of a timing recovery algorithm followed by a zero-forcing detector. Simulation results demonstrate accurate symbol timing offset estimation and improved symbol error rate performance compared to existing time encoding receivers.

Heart failure (HF) affects 11.8% of adults aged 65 and older, reducing quality of life and longevity. Preventing HF can reduce morbidity and mortality. We hypothesized that artificial intelligence (AI) applied to 24-hour single-lead electrocardiogram (ECG) data could predict the risk of HF within five years. To research this, the Technion-Leumit Holter ECG (TLHE) dataset, including 69,663 recordings from 47,729 patients, collected over 20 years was used. Our deep learning model, DeepHHF, trained on 24-hour ECG recordings, achieved an area under the receiver operating characteristic curve of 0.80 that outperformed a model using 30-second segments and a clinical score. High-risk individuals identified by DeepHHF had a two-fold chance of hospitalization or death incidents. Explainability analysis showed DeepHHF focused on arrhythmias and heart abnormalities. This study highlights the feasibility of deep learning to model 24-hour continuous ECG data, capturing paroxysmal events essential for reliable risk prediction. Artificial intelligence applied to single-lead Holter ECG is non-invasive, inexpensive, and widely accessible, making it a promising tool for HF risk prediction.

We present an algorithm based on the alternating direction method of multipliers (ADMM) for solving nonlinear matrix decompositions (NMD). Given an input matrix $X \in \mathbb{R}^{m \times n}$ and a factorization rank $r \ll \min(m, n)$, NMD seeks matrices $W \in \mathbb{R}^{m \times r}$ and $H \in \mathbb{R}^{r \times n}$ such that $X \approx f(WH)$, where $f$ is an element-wise nonlinear function. We evaluate our method on several representative nonlinear models: the rectified linear unit activation $f(x) = \max(0, x)$, suitable for nonnegative sparse data approximation, the component-wise square $f(x) = x^2$, applicable to probabilistic circuit representation, and the MinMax transform $f(x) = \min(b, \max(a, x))$, relevant for recommender systems. The proposed framework flexibly supports diverse loss functions, including least squares, $\ell_1$ norm, and the Kullback-Leibler divergence, and can be readily extended to other nonlinearities and metrics. We illustrate the applicability, efficiency, and adaptability of the approach on real-world datasets, highlighting its potential for a broad range of applications.

The increasing complexity of modern power systems, driven by the integration of inverter-based and distributed energy resources, challenges the reliability of conventional protection schemes and motivates the use of machine learning for protection tasks. However, published results are often difficult to compare because datasets, sensing assumptions, and decision horizons vary across studies. This paper presents a controlled comparison of machine learning models for fault classification (FC) and fault localization (FL) under identical sensing, timing, and validation conditions on a common electromagnetic transient dataset, using decision windows of 10-50 ms to reflect protection-relevant time scales. For FC, the best-performing nonlinear models achieve F1 scores above 0.98 already at 10 ms, while lower-capacity models degrade at shorter horizons but improve with longer windows, indicating that relevant fault-type information is already present in the earliest transient. For FL, the top-performing models reach a stable localization error of about 10 % of normalized line length across all evaluated horizons, while weaker models form a clearly separated second performance tier. Line-resolved analysis shows that localization accuracy varies across grid segments, indicating topology-dependent difficulty rather than insufficient temporal context alone. These findings provide a controlled reference for comparing machine learning models across two protection tasks with fundamentally different information requirements.

Hybrid Analog and Digital (HAD) architectures significantly reduce hardware overhead but introduce severe dimensionality compression, which strips the Spatial Covariance Matrix (SCM) of the degrees of freedom required for high-resolution Direction-of-Arrival (DoA) estimation. This challenge is further compounded by passive Butler-matrix implementations of Discrete Fourier Transform (DFT) analog beamforming, which avoid active phase shifters and amplifiers. In this paper, we propose a Generalized Least Squares (GLS) framework that exploits the Cauchy-like displacement structure that arises after DFT beamforming. By leveraging this structure, we develop a highly efficient numerical technique to recover the SCM for uniform linear arrays with a complexity of $\mathcal{O}(N_{\text{RF}}^2 N_x)$, where $N_x$ is the number of antennas and $N_{\text{RF}}$ the number of RF-chains. Simulations demonstrate that our estimator approaches the Cramér-Rao Bound (CRB) while outperforming state-of-the-art methods.